Analytics Engineer I, Commercialization

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Position Summary The Commercial Data & AI HYD team is seeking an Analytics Engineer I to help build and support the analytics layer on Databricks that connects curated data to the dashboards, self-service BI, and advanced analytics used across the business. This role is responsible for developing well-modeled datasets, metrics, and semantic assets that are easy for analysts and business users to consume. This role operates as part of a deployed engineering team, working alongside an assigned Commercial business domain (e.g., Sales, Marketing, Market Access, or Medical Affairs) under the guidance of senior engineers to help keep the analytics layer aligned with that domain's needs. The successful candidate will help analyze business and analytics requirements and build, test, and integrate the application components, tools, and utilities that support commercial reporting and analytics consumption, applying data modeling, SQL transformation, data quality, documentation, and governance practices under the guidance of senior engineers on the team. This is an early-career role that offers strong exposure to Databricks-based analytics engineering, with the opportunity to build technical depth and grow into more senior data modeling and platform responsibilities over time. Key Responsibilities Design, build, and support analytics-ready data products, curated datasets, and reusable transformation assets within a modern lakehouse environment to enable commercial reporting, self-service analytics, and business insights. Translate business and analytics requirements into clear data specifications, dimensional models, metrics definitions, and dataset readiness criteria aligned to internal modeling and delivery standards. Develop and maintain scalable SQL- and Python-based transformation logic that integrates structured and semi-structured pharma datasets such as claims, patient, sales, payer, HUB, and specialty pharmacy data. Apply internally developed accelerators, reusable code patterns, templates, and engineering guardrails to improve development speed, consistency, and maintainability across data products. Create analytics-friendly models, including fact and dimension tables, cross-domain joins, slowly changing dimensions, and business-rule-driven metrics that support dashboard, reporting, and advanced analytics consumption.